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Maintenance is a critical business function with a great impact on economic, environmental and social aspects. However, maintenance decisions' planning has been driven by merely economic and technical measures with inadequate consideration of environmental and social dimensions. This paper presents a review of the literature pertaining to sustainable maintenance decision-making models supported by a bibliometric analysis that seeks to establish the evolution of this research over time and identify the main research clusters.

A systematic literature review, supported with a bibliometric and network analysis, of the extant studies is conducted. The relevant literature is categorized based on which sustainability pillar, or possibly multiple ones, is being considered with further classification outlining the application area, modeling approach and the specific peculiarities characterizing each area.

The review revealed that maintenance and sustainability modeling is an emerging area of research that has intensified in the last few years. This fertile area can be developed further in several directions. In particular, there is room for devising models that are implementable, based on reliable and timely data with proven tangible practical results. While the environmental aspect has been considered, there is a clear scarcity of works addressing the social dimension. One of the identified barriers to developing applicable models is the lack of the required, accurate and timely data.

This work contributes to the maintenance and sustainability modeling research area, provides insights not previously addressed and highlights several avenues for future research. To the best of the authors' knowledge, this is the first review that looks at the integration of sustainability issues in maintenance modeling and optimization.

Quality 4.0 is an emerging research topic concerned with rethinking how quality management needs to be adopted in the digital era. The purpose of this research is to conduct a systematic review on the state of the research in the field of Industry 4.0 impact on improving quality management aspects and how technology can be leveraged to enhance its practices.

A systematic review of the literature published in the last 5 years is conducted. 52 papers were selected, mapped based on the technology they focused on and categorized based on the addressed quality aspects.

The review revealed various areas where quality management can benefit from Industry 4.0 technologies, identified several research gaps and suggested new directions for future research. Firstly, the literature provided some insights about industry 4.0 potential contributions but lacks further detail on the exact applications and solutions through use cases and case studies. Secondly, there has been a focus on the potential benefits provided for quality control while there is a clear scarcity in terms of the other quality management tools and methodologies. Thirdly, there is a lack of studies on economic analysis or detailed impacts on quality costs that justifies the substantial investments needed. Finally, there is a need for including more studies about the mapping and integration of ISO 9001 requirements and Industry 4.0 features.

This is the first attempt to conduct a comprehensive review on the ways industry 4.0 technologies can be leveraged for the field of quality management. Based on this review, several directions for further research in this area are identified.

Sets out to develop an integrated model that considers simultaneously inventory production decisions, PM schedule, and warranty policy for a deteriorating system that experiences shifts to an out of control state. The time to shift follows a general probability distribution with increasing hazard rate, so that time‐based PM is effective in improving the system reliability.

A profit function is used to model the production system. Optimization techniques are used to generate optimal solutions for the problem. Although global optimality cannot be guaranteed, empirical results show that global optimal solutions are obtained.

The integrated model provides decisions on inventory levels, production run length, and PM schedule simultaneously. It is illustrated through numerical examples that investment in PM can lead to savings in warranty claims for repairable products. As a result, the overall profit per unit, in certain cases, is higher with PM than without PM.

The production system is taken, numerical examples are presented and a sensitivity analysis is conducted to gain more insight into the developed model. In particular, the numerical analysis shows that a better PM program reduces warranty claims.

In addition to the joint optimization of production/inventory decisions and PM schedule, such models can be very useful in making resource allocation decisions between warranty and PM programs. It is clear from the numerical analysis that a better PM program reduces warranty claims.

The paper provides a joint optimization of production inventory decisions and the PM schedule for a system subject to a time to shift that follows a general probability distribution. Previous research considered only an exponential distribution and did not consider PM.

A brief article considering the nature of total productive maintenance (TPM) and reliability‐centered maintenance (RCM) and the relationship between the two. Notes equipment management and empowerment of employees as key features of TPM. Sees the development of an effective preventive maintenance program as essential to effective equipment management and suggests that RCM is central to the development of such a program. Points out that, if implemented within the framework of TPM, RCM can help achieve better results from the TPM implementation.

The aim of this paper is to present a synthetic chart based on the non‐central chi‐square statistic that is operationally simpler and more effective than the joint X¯ and R chart in detecting assignable cause(s). This chart will assist in identifying which (mean or variance) changed due to the occurrence of the assignable causes.

The approach used is based on the non‐central chi‐square statistic and the steady‐state average run length (ARL) of the developed chart is evaluated using a Markov chain model.

The proposed chart always detects process disturbances faster than the joint X¯ and R charts. The developed chart can monitor the process instead of looking at two charts separately.

The most important advantage of using the proposed chart is that practitioners can monitor the process by looking at only one chart instead of looking at two charts separately.

Examines the application and use of statistical process control (SPC) tools to improve the quality of maintenance work. Identifies areas in maintenance where SPC tools can be used. Gives an example that demonstrates the use of these tools in an integrated fashion and outlines the data needed to utilize these tools effectively. Finally, the need for an integration of the maintenance information system (MIS) and the quality information system (QIS) is highlighted.

The purpose of this paper is to develop a procedure for deciding on the limits of initial clearance to which tubesheet holes can be enlarged before replacement in heat exchanger maintenance.

An empirical model that relates the hardness of roller expanded tubes and ligaments to initial clearance is developed from experimental data to predict the extent of tolerable levels of over‐enlargement of tubesheet holes before the joint loses its structural integrity. The developed model serves as an additional criterion to decide whether to keep or discard a tubesheet having over‐enlarged holes during heat exchanger maintenance.

The current industrial criterion does not directly include the effect of material degradation for over‐enlarged holes. The empirical models indicate that both tubes and ligaments suffer strain hardenings. A new procedure is proposed to supplement the current industrial criterion.

The procedure will assist maintenance managers/engineers in deciding on the replacement of heat exchanger tubesheets. The combined criteria will have an impact on the cost of heat exchanger maintenance and plant downtime.

The procedure proposed in this paper adds to the industrial criterion another one that caters for the degradation in properties that the tube, tubesheet, and surrounding ligaments will be subjected to during roller expansion. In other words, it considers the strain hardening of the tube and tubesheet materials in setting the initial clearance between the tube and tubesheet that ensures adequate joint integrity.

The aim of this research is to determine the optimal upgrade and preventive maintenance actions that minimize the total expected cost (maintenance costs+penalty costs).

The problem is a four‐parameter optimization with two parameters being k‐dimensional. The optimal solution is obtained by using a four‐stage approach where at each stage a one‐parameter optimization is solved.

Upgrading action is an extra option before the lease of used equipment, in addition to preventive maintenance action. Upgrading action makes equipment younger and preventive maintenance action lowers the ROCOF.

There is a growing trend towards leasing equipment rather than owning it. The lease contract contains penalties if the equipment fails often and repairs are done within reasonable time period. This implies that the lessor needs to look at optimal preventive maintenance strategies in the case of new equipment lease, and upgrade actions plus preventive maintenance in the case of used equipment lease. The paper deals with this topic and is of great significant to business involved with leasing equipment.

Nowadays many organizations are interested in leasing equipment and outsourcing maintenance. The model in this paper addresses the preventive maintenance problem for leased equipment. It provides an approach to dealing with this problem.

This paper aims to discuss and bring to the attention of researchers and practitioners the data management issues relating to condition‐based maintenance (CBM) optimization.

The common data quality problems encountered in CBM decision analyses are investigated with a view to suggesting methods to resolve these problems. In particular, the approaches for handling missing data in the decision analysis are reviewed.

This paper proposes a data structure for managing the asset‐related maintenance data that support CBM decision analysis. It also presents a procedure for data‐driven CBM optimization comprising the steps of data preparation, model construction and validation, decision‐making, and sensitivity analysis.

Analysis of condition monitoring data using the proportional hazards modeling (PHM) approach has been proved to be successful in optimizing CBM decisions relating to motor transmission equipment, power transformers and manufacturing processes. However, on many occasions, asset managers still make sub‐optimal decisions because of data quality problems. Thus, mathematical models by themselves do not guarantee that correct decisions will be made if the raw data do not have the required quality. This paper examines the significant issues of data management in CBM decision analysis. In particular, the requirements of data captured from two common condition monitoring techniques – namely vibration monitoring and oil analysis – are discussed.

This paper offers advice to asset managers on ways to avoid capturing poor data and the procedure for manipulating imperfect data, so that they can assess equipment conditions and predict failures more accurately. This way, the useful life of physical assets can be extended and the related maintenance costs minimized. It also proposes a research agenda on CBM optimization and associated data management issues.